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Method for obtaining surface normal vector of automobile workpiece based on point cloud model

A workpiece surface and point cloud model technology, which is applied in the field of calculating the normal vector of the surface of the automobile workpiece, can solve the problems of large calculation amount, slow calculation speed, and low accuracy of the normal vector of the surface, and achieve the effect of reducing the calculation amount

Inactive Publication Date: 2017-07-07
浙江优迈德智能装备有限公司
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0003] The purpose of the present invention is to solve the shortcomings of large calculation amount, slow calculation speed and low accuracy in obtaining the surface normal vector in the existing painting process, and propose a method for obtaining the surface normal vector of the automobile workpiece based on the point cloud model

Method used

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  • Method for obtaining surface normal vector of automobile workpiece based on point cloud model
  • Method for obtaining surface normal vector of automobile workpiece based on point cloud model
  • Method for obtaining surface normal vector of automobile workpiece based on point cloud model

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specific Embodiment approach 1

[0017] Specific implementation mode one: the specific steps of a method for obtaining the surface normal vector of an automobile workpiece based on a point cloud model in this embodiment are as follows:

[0018] Step 1. Scan the automobile workpiece to obtain the point cloud image, and use the octree method to compress the point cloud image to obtain the compressed point cloud image; where the automobile workpiece is sprayed is there; figure 1 ;

[0019] Step 2, performing spatial position encoding on the compressed point cloud image obtained in step 1, to obtain the spatial neighbor relationship between points;

[0020] Step 3, using the spatial neighbor relationship between points to obtain the normal vector according to the least square method.

specific Embodiment approach 2

[0021] Specific embodiment two: the difference between this embodiment and specific embodiment one is: scan the automobile workpiece in the step one, obtain the point cloud map, adopt the method of octree to compress the point cloud map, obtain the compressed point cloud map; specifically The process is:

[0022] Step 11, traversing the point cloud data, using the octree algorithm to divide the point cloud data; the specific process is:

[0023] Use a recursive method to divide the obtained point cloud image data. Before dividing, first determine the maximum number of point clouds in each subspace m; the value of m is a positive integer;

[0024] Divide the space where the parent node is located into eight subspaces, and determine the scope of each subspace; assuming that the minimum point coordinates of the space where the parent node is located are (x, y, z) (consistent with the right-hand rule), in the three The lengths on the axis are l x , l y , l z ;

[0025] Then, ...

specific Embodiment approach 3

[0031] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is that in the step two, the compressed point cloud image obtained in step one is encoded for the spatial position, and the spatial neighbor relationship between points is obtained; The specific process is:

[0032] In order to facilitate the query of K-nearest neighbors, according to the characteristics of the octree structure, the nodes can be encoded in octal, and any node can be associated with octal data; that is:

[0033] Q=q n-1 8 n-1 +q n-2 8 n-2 +…+q 2 8 2 +q 1 8 1 +q 0 8 0

[0034] In the formula, Q is the node number, n is the node depth, that is, the number of space divisions, and q j It is octal data, the value range of j is 0≤j≤n-1; the value of n is a positive integer; in this way, the position information of each subspace in the space is encoded. The access path from the root node to the corresponding leaf node can be obtained from the code. ...

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Abstract

A method for obtaining a surface normal vector of an automobile workpiece based on a point cloud model is disclosed. The invention relates to the method for obtaining the surface normal vector of the automobile workpiece based on the point cloud model. The invention aims to solve the defects of a large calculation amount, a slow calculation speed and low accuracy of curved surface normal vector calculation in an existing painting process. The method comprises the steps of (1) scanning the automobile workpiece, obtaining a point cloud image, compressing the point cloud image by using an octree method, and obtaining a compressed point cloud image, (2) carrying out space position encoding on the compressed point cloud image and obtaining a space neighborhood relation between points, and (3) obtaining the normal vector according to a least squares method by using the space neighborhood relation between the points. The method is used in the field of spraying robots.

Description

technical field [0001] The invention relates to a method for calculating the surface normal vector of an automobile workpiece based on a point cloud model. Background technique [0002] With the development of the industrial level, spraying robots occupy an increasingly important position in the automobile and other industries. It is also a difficult point to realize the automation of painting. Keeping the spray gun head vertically aligned with the automobile workpiece during the painting process is a key factor affecting the quality of the painting. the elements of. In the current painting process, there are mainly two methods to obtain the surface normal vector, one is based on the STL model, and the other is based on the point cloud model. The first method is generally estimated based on the normal vectors of the triangles around the desired point, so it is necessary to establish a topological relationship for all the triangles on the surface to facilitate the search for...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T17/00G06T17/30
Inventor 林伟阳陈坚鸿李湛卫作龙于兴虎
Owner 浙江优迈德智能装备有限公司
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